Identifying active transcription factors and kinases from expression data using pathway queries
Department of Informatics, Ludwig-Maximilians-Universität Amalienstraße 17, 80333 München, Germany
*To whom correspondence should be addressed.
Motivation: Although progress has been made identifying regulatory relationships from expression data in general, only few methods have focused on detecting biological mechanisms like active pathways using a single measurement. This is of particular importance when only few measurements are available, e.g. if special cell types or conditions are under investigation. Here we present a method to test user specified hypotheses (pathway queries) on expression data where prior knowledge is given in the form of networks and functional annotations. Based on this method, we develop a scoring function to identify active transcription factors or kinases, thus making a first step toward explaining the measured expression data.
Results: We apply the algorithm to the Rosetta Yeast Compendium dataset, finding that in many cases the results are in concordance with biological knowledge. We were able to confirm that transcription factors and to a lesser degree, kinases identified by our method play an important role in the biological processes affected by the respective knockouts. Furthermore, we show that correlation of inferred activities can provide evidence for a physical interaction or cooperation of transcription factors where correlation of plain expression data fails to do so.
Contact: florian.sohler{at}bio.ifi.lmu.de
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  S. Bruckner, F. Huffner, R. M. Karp, R. Shamir, and R. Sharan Torque: topology-free querying of protein interaction networks Nucleic Acids Res., July 1, 2009; 37(suppl_2): W106 - W108. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. A. Fulcher, S. T. Hashimi, E. L. Levroney, M. Pang, K. B. Gurney, L. G. Baum, and B. Lee Galectin-1-Matured Human Monocyte-Derived Dendritic Cells Have Enhanced Migration through Extracellular Matrix J. Immunol., July 1, 2006; 177(1): 216 - 226. [Abstract] [Full Text] [PDF]
|
|